DBMS > FatDB vs. Google Cloud Bigtable vs. mSQL vs. SAP SQL Anywhere vs. STSdb

System Properties Comparison FatDB vs. Google Cloud Bigtable vs. mSQL vs. SAP SQL Anywhere vs. STSdb

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

mSQL (Mini SQL) is a simple and lightweight RDBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Key-value store
Wide column store

Relational DBMS

Key-value store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/bigtable

hughestech.com.au/products/msql

www.sap.com/products/technology-platform/sql-anywhere.html

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigtable/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

FatCloud

Google

Hughes Technologies

SAP

formerly Sybase

STS Soft SC

Initial release

2012

2015

1994

1992

2011

Current release

4.4, October 2021

17, July 2015

4.0.8, September 2015

License

Commercial or Open Source

commercial

free licenses can be provided

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Server operating systems

Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

.NET Client API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

via applications

yes, in C/C++, Java, .Net or Perl

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Can implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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